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A personal knowledge management metamodel based on semantic analysis and social information

Authors
  • López-Quintero, J. F.1
  • Cueva Lovelle, J. M.2
  • González Crespo, R.3
  • García-Díaz, V.2
  • 1 Universidad de Oviedo, Oviedo, Spain , Oviedo (Spain)
  • 2 Universidad de Oviedo, Departamento de Informática, Oviedo, Spain , Oviedo (Spain)
  • 3 Universidad Internacional de La Rioja (UNIR), Escuela Superior de Ingeniería y Tecnología, Logroño, Spain , Logroño (Spain)
Type
Published Article
Journal
Soft Computing
Publisher
Springer-Verlag
Publication Date
Nov 16, 2016
Volume
22
Issue
6
Pages
1845–1854
Identifiers
DOI: 10.1007/s00500-016-2437-y
Source
Springer Nature
Keywords
License
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Abstract

This article describes the development of a functional architecture for personal knowledge management (PKM), defined from the lessons-learnt concept registered in a mass-use social network analyzed with an algorithm of machine learning. This functional architecture applies, in practical manner, the implementation of a registry system of the personal lessons learnt in the cloud through a Facebook social network. The process starts by acquiring data from the connection to a non-relational database (NoSql) in Amazon’s SimpleDB and to which a complementary analysis algorithm of machine learning has been configured for the semantic analysis of the information registered from lessons learnt and, thus, to study the generation of organizational knowledge management from PKM. The result is the design of a functional architecture that permits integrating the Web 2.0 application and a semantic analysis algorithm from unstructured information by applying machine learning techniques.

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